Motor Vehicle Crashes and Land Use: Empirical Analysis from Hawaii

Abstract

At first glance, the relationship between crashes and land use appears obvious. Various types of land uses tend to generate and attract different types of trips, and trip-making behavior affects the nature and volume of traffic. As the use of land intensifies, it does not seem unreasonable to expect that the potential exposure to crashes would also increase. Yet upon closer inspection, it is evident that crashes are more a function of the characteristics of drivers and travelers than the underlying uses of land. Using comprehensive police crash data linked to a land use database, the relationships between land use and automobile crashes in Hawaii are investigated. Recent developments in geographic information system technology and the availability of spatial databases provide a rich source of information with which to investigate the relationships between crashes and the environments in which they occur. Although the patterns in terms of crashes and the underlying use of land are difficult to describe, the implications of these findings for planners, developers, and those interested in traffic safety are apparent.

The influence of urban land-use on non-motorised transport casualties

“…In other words, locating trip generators and trip attractors close to each other could reduce the need to travel by car, thus encouraging walking and cycling. Kim and Yamashita (2002) compared the vehicle-to-vehicle crashes with vehicle-to-pedestrian and vehicle-to-bicycle per acre land-use category for Honolulu over a 10-year period. They found that vehicle-to-vehicle crashes were highest in commercial and industrial (6.62/10 acre year), visitor lodging (5.15/ 10 acre year) and manufacturing and industry (3.67/10 acre year).…”

A Space–Time Multivariate Bayesian Model to Analyse Road Traffic Accidents by Severity

“…For instance, Miaou and Lum () compared this technique with conventional linear regression to assess accidents and highway geometric design relationships, whereas Jovanis and Chang () used Poisson regression to assess the effects of travel mileage on accident occurrence. Other examples of previous work include Kim and Yamashita () and Graham and Glaister () who focused on the association between road traffic accidents and potential risk factors.…”

Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data

“…Honolulu census tract data have been utilised to explain spatial variation in motor vehicle crashes (Levine et al, 1995b). Kim and Yamashita (2002) conducted an empirical analysis of motor vehicle crashes and land use variables with the aid of geographic information systems (GIS). Hadayeghi et al (2003) developed a series of macro-level crash prediction models that estimate the number of crashes in planning zones in the city of Toronto as a function of zonal characteristics.…”